The idea we employ is so-called deep unfolding: which constructs a layer-wise structure by unfolding an iterative algorithm and tunes parameters in the structure such as step-size and regularization coefficients by standard deep learning techniques. Originally developed to combining the advantages of model-based methods and deep neural networks (DNNs) [1]}, it has been used in signal and image processing and communication systems [2]} as well as average consensus problems [3]}, [4]}.
To apply this deep unfolding to our control problem, we consider the discrete-time state transition as an iterative algorithm and consider the control inputs as tuning parameters.
